Type 2 diabetes (T2D) is a major cause of morbidity and mortality in the USA and worldwide. While disease prevalence varies with age, gender, and population, in 2007 an estimated 23.5 million Americans (10.7%) age 20 years or older and 12.2 million Americans (23.1%) age 60 years or older suffered from diabetes. Substantial evidence exists supporting a genetic component in the etiology of T2D and T2D-related quantitative traits (QTs). Our overall goal is to identify genetic variants that predispose to T2D and that are responsible for variability in T2D-related QTs. In the current proposal we seek to build on our recent successes, particularly identification of loci associated with T2D and selected QTs, by moving from locus to gene to determine the specific causal genes responsible for these association signals. Although some loci identified by genome-wide association (GWA) studies strongly suggest a nearby gene of likely diabetes relevance, numerous robustly replicated association signals do not contain obvious links to underlying genes or known T2D pathways. Furthermore, while currently funded deep resequencing and GWA studies of lower frequency variants will suggest many candidate causal T2D variants, functional studies are required to determine which variants have a biological effect. Specifically, we will test risk variants identified from deep T2D resequencing using follow-up association studies in >28,000 samples, determine the metabolic and functional consequences of T2D- and QT-associated rare potentially causal variants, and identify the most likely candidate causal genes and biological mechanisms at T2D- and QT-associated loci using gene knockdown and over expression studies. The proposed project combines outstanding resources of well- characterized samples with expertise in functional, genome, and statistical analysis. Successful identification of genes and variants underlying risk of T2D and related QTs has the potential to reduce the impact of the current T2D epidemic by supporting identification of novel treatments, enabling better targeting of preventive and therapeutic approaches, and providing more accurate T2D risk prediction.